Along with development of an information technology, an information security problem becomes increasingly serious, wherein personal identification is an important part in the field of information security.
Face authentication is a face recognition form, judges whether two given face images belong to the same person or not by extracting characteristics of the two given face images, and has the characteristics of directness, friendliness and convenience compared with another biological feature authentication technology.
Face authentication mainly includes three parts, i.e. face detection, eye location and feature extraction and authentication.
A face belongs to a typical three-dimensional non-rigid object, and face authentication is usually based on a view pattern, and is easily influenced by an uncertain factor such as light, a posture and an expression, so that face recognition is extremely challenging. In addition, face recognition relates to multiple fields of computer science, pattern recognition, artificial intelligence and the like, has broad application market and huge commercial potential, and thus has drawn attention of more and more companies and research institutes.
A face authentication solution in a related technology will be introduced below.
Early face recognition algorithms all take parameters such as distances and ratios of face feature points as features and suppose that an image background is undiversified or there is no background. In the last few decades, researches on face recognition have made a great progress, domestic and abroad researchers proposed some methods about face recognition, and although different researchers may summarize and classify existing face recognition algorithms from different points, the face recognition algorithms are feature extraction methods, and forms of features are closely related to classification rules. In the disclosure, face recognition methods are substantially divided into: a geometric-feature-based method, an algebraic-feature-based method and a texture-feature-based method.
(1) The Geometric-feature-based Method
According to the method, a face is represented by a geometric feature vector, the geometric feature vector is a feature vector based on shapes and geometric relationships of face organs, and its components usually include a Euclidean distance between two points of the face, a curvature, an angle and the like. Main algorithms include an integral projection method, elastic template matching and the like.
The method utilizes positions of different feature points of eyes, nose, mouth and the like of the face, and position information of these points is difficult to obtain, and is greatly influenced by expressions and light.
(2) The Algebraic-feature-based Method
According to the method, an overall attribute of a face pattern is considered, for example, a Principal Component Analysis (PCA) method and a Linear Discriminant Analysis (LDA) method.
The PCA method performs PCA on face images which is taken as vectors, reduces the face images to a certain dimension to obtain feature vectors, and balances a similarity between two face images by a cosine value of an included angle between the two vectors.
A basic thought of the LDA method is to seek for a projection space to maximize a ratio of an inter-sample class discrete degree and an intra-sample class discrete degree after projection, so that a subspace formed by LDA aims to achieve optimal divisibility, and is more suitable than PCA for recognition.
The biggest problem of these methods is a singularity problem of a matrix, and moreover, overall information of a face is utilized, and is easily influenced by expressions, light and the like.
(3) The Texture-feature-based Method
The method extracts texture information on a face, and converts a face recognition problem into texture classification, and common extracted texture features include a Local Binary Pattern (LBP) feature, a Gabor feature and a Pattern of Oriented Edge Magnitude (POEM) feature.
The three feature extraction manners may all extract global information of a face image without influence of an occlusion. However, LBP features are poor in light stability particularly when light non-uniformly changes. Gabor features are less influenced by light, but extraction of the Gabor features of each point on the whole face is low in speed. Compared with LBP feature extraction, POEM feature extraction calculates a gradient map at first, and an LBP operator is applied on such a basis, so that influence of light is also reduced; POEM features are extracted and calculated according to blocks, so that a speed is higher; and however, it is necessary to design positions and sizes of the feature blocks. In brief, Gabor features are extracted by points on the face image, while POEM features are extracted by blocks on the face image.
Defects of the Related Technology
1: Expressiveness of a face which is a complex and changing non-rigid object and influence of a change in an outside condition in an image acquisition process make face authentication difficult, and an ideal effect is unlikely to be achieved by adoption of only one face feature for face authentication.
2: Complexity of a face pattern requires low time complexity of an authentication algorithm. When Gabor features are used, thousands of Gabor feature points are required for storage, and a calculation burden is very heavy, so that it is unfavorable for large-scale face database recognition and authentication. A Gabor-feature-based face authentication method requires extraction of a large number of Gabor feature points and requires a large amount of complex calculation, so that an extraction speed is low. In addition, Gabor feature points extracted by the method include many highly-related feature points, which may cause low discriminability during face authentication.
3: Adoption of POEM feature exampling also has such a limit that division of image blocks is fixed. Since a face is greatly influenced by an environment and a posture, extraction of more and more effective features from the face may not be ensured if positions and sizes of feature blocks are fixed.
The method is lower in calculation burden during extraction of POEM feature blocks, so that its feature point extraction speed is high. However, positions and sizes of the POEM feature blocks are usually required to be fixed in the related technology, and the POEM feature blocks with the fixed positions and sizes may not be the most ideal feature blocks. In addition, the POEM feature blocks extracted by the method include many highly-related feature blocks, which may cause low discriminability during face authentication.
For the problem of difficulty of a face authentication method in the related technology in combination of efficiency and recognition rate, there is yet no effective solution.